Neural Modeling: The STAA Approach
This section provides an introduction to computer-implemented connectionist neural models. It explains how sensory, motor, and cognitive states are represented at the neural level and how these states can be processed in neural networks. Supervised learning is illustrated through a sensorimotor association example and unsupervised learning through a self-organizing network example, both using vowel representations. This chapter is intended to provide a basic understanding of how our central nervous system works by modeling it as a neural network with interconnected buffers of neurons, and recurrently connected buffers that maintain short-term memories.
KeywordsConnectionism Nodes Link weights Neural network Neural learning Kohonen network Neural self-organization
Sections 6.1 and 6.2
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